Symmetry-Breaking Convergence Analysis of Certain Two-layered Neural Networks with ReLU nonlinearity
Authors: Yuandong Tian
ICLR 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Simulations verify our theoretical analysis. ... Sec. 5 shows that simulation results are consistent with theoretical analysis. |
| Researcher Affiliation | Industry | Yuandong Tian Facebook AI Research yuandong@fb.com |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link for open-sourcing its code. |
| Open Datasets | No | The paper assumes that the input x follows Gaussian distribution (synthetic data assumption) but does not mention the use of any publicly available or open real-world dataset with access information. It states: "We assume that the input x follow Gaussian distribution." and "We prepare the input data X with standard Gaussian distribution". |
| Dataset Splits | No | The paper does not mention specific training, validation, or test dataset splits for any real-world data. It analyzes theoretical dynamics with assumed Gaussian input distribution. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory) used for running its simulations. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers used for its simulations or analysis. |
| Experiment Setup | No | The paper describes the theoretical setup and assumptions (e.g., Re LU nonlinearity, Gaussian input, teacher-student setting) but does not provide specific experimental setup details such as hyperparameters (learning rate, batch size, epochs, optimizers) for training a neural network model. It focuses on the dynamics analysis. |